


from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase     							=			{"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}


if TYPE_CHECKING:
    from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
    import sys

    __lowerCAmelCase     							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










"""simple docstring"""





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__   =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__   =  {
    "allenai/led-base-16384": 16_384,
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = LEDTokenizer
      _SCREAMING_SNAKE_CASE							      = ['input_ids', 'attention_mask']





      def __init__(			self ,		lowercase=None ,		lowercase=None ,		lowercase=None ,		lowercase="replace" ,		lowercase="<s>" ,		lowercase="</s>" ,		lowercase="</s>" ,		lowercase="<s>" ,		lowercase="<unk>" ,		lowercase="<pad>" ,		lowercase="<mask>" ,		lowercase=False ,		lowercase=True ,		**lowercase ,		)						->	Any:
            super().__init__(
                lowercase ,		lowercase ,		tokenizer_file=lowercase ,		errors=lowercase ,		bos_token=lowercase ,		eos_token=lowercase ,		sep_token=lowercase ,		cls_token=lowercase ,		unk_token=lowercase ,		pad_token=lowercase ,		mask_token=lowercase ,		add_prefix_space=lowercase ,		trim_offsets=lowercase ,		**lowercase ,		)

            lowerCAmelCase    		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
            if pre_tok_state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                  lowerCAmelCase    		=			getattr(lowercase ,		pre_tok_state.pop("""type"""					)					)
                  lowerCAmelCase    		=			add_prefix_space
                  lowerCAmelCase    		=			pre_tok_class(**lowercase					)

            lowerCAmelCase    		=			add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            lowerCAmelCase    		=			"""post_processor"""
            lowerCAmelCase    		=			getattr(self.backend_tokenizer ,		lowercase ,		lowercase					)
            if tokenizer_component_instance:
                  lowerCAmelCase    		=			json.loads(tokenizer_component_instance.__getstate__()					)

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                        lowerCAmelCase    		=			tuple(state["""sep"""]					)
                  if "cls" in state:
                        lowerCAmelCase    		=			tuple(state["""cls"""]					)

                  lowerCAmelCase    		=			False

                  if state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                        lowerCAmelCase    		=			add_prefix_space
                        lowerCAmelCase    		=			True

                  if state.get("""trim_offsets""" ,		lowercase					) != trim_offsets:
                        lowerCAmelCase    		=			trim_offsets
                        lowerCAmelCase    		=			True

                  if changes_to_apply:
                        lowerCAmelCase    		=			getattr(lowercase ,		state.pop("""type"""					)					)
                        lowerCAmelCase    		=			component_class(**lowercase					)
                        setattr(self.backend_tokenizer ,		lowercase ,		lowercase					)





      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def        _snake_case     (			self					)						->	str:
            if self._mask_token is None:
                  if self.verbose:
                        logger.error("""Using mask_token, but it is not set yet."""					)
                  return None
            return str(self._mask_token					)





      @mask_token.setter
      def        _snake_case     (			self ,		lowercase					)						->	Optional[int]:
            lowerCAmelCase    		=			AddedToken(lowercase ,		lstrip=lowercase ,		rstrip=lowercase					) if isinstance(lowercase ,		lowercase					) else value
            lowerCAmelCase    		=			value





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._batch_encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	Tuple[str]:
            lowerCAmelCase    		=			self._tokenizer.model.save(lowercase ,		name=lowercase					)
            return tuple(lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase=None					)						->	Any:
            lowerCAmelCase    		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                  return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[int]:
            lowerCAmelCase    		=			[self.sep_token_id]
            lowerCAmelCase    		=			[self.cls_token_id]

            if token_ids_a is None:
                  return len(cls + token_ids_a + sep					) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = PaddingStrategy.DO_NOT_PAD ,		lowercase = None ,		lowercase = None ,		)						->	dict:
            lowerCAmelCase    		=			super()._pad(
                encoded_inputs=lowercase ,		max_length=lowercase ,		padding_strategy=lowercase ,		pad_to_multiple_of=lowercase ,		return_attention_mask=lowercase ,		)

            # Load from model defaults
            if return_attention_mask is None:
                  lowerCAmelCase    		=			"""attention_mask""" in self.model_input_names

            if return_attention_mask and "global_attention_mask" in encoded_inputs:
                  lowerCAmelCase    		=			encoded_inputs[self.model_input_names[0]]
                  # `global_attention_mask` need to have the same length as other (sequential) inputs.
                  lowerCAmelCase    		=			len(encoded_inputs["""global_attention_mask"""]					) != len(lowercase					)

                  if needs_to_be_padded:
                        lowerCAmelCase    		=			len(lowercase					) - len(encoded_inputs["""global_attention_mask"""]					)

                        if self.padding_side == "right":
                              # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                              lowerCAmelCase    		=			(
                                  encoded_inputs["""global_attention_mask"""] + [-1] * difference
                              )
                        elif self.padding_side == "left":
                              lowerCAmelCase    		=			[-1] * difference + encoded_inputs[
                                  """global_attention_mask"""
                              ]
                        else:
                              raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

            return encoded_inputs






